Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction With the Use of Electrocardiographic Artificial Intelligence: A Cost-Effectiveness Approach
Abstract
Background The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening. Methods In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan. Patients were stratified into LVD (left ventricular ejection fraction ≤ 40%) risk groups according to a previously developed ECG algorithm. The performance of AI-ECG was used to conduct a cost-effectiveness analysis of LVD screening compared with no screening. Incremental cost-effectiveness ratio (ICER) and sensitivity analyses were used to examine the cost-effectiveness and robustness of the results. Results Among the 29,137 patients, the algorithm demonstrated areas under the receiver operating characteristic curves of 0.984 and 0.945 for detecting LVD within 28 days in the 2 hospital cohorts. For patients not initially scheduled for ECG, the algorithm predicted future echocardiograms (high-risk, 46.2%; medium-risk, 31.4%; low-risk, 14.6%) and LVD (high-risk, 26.2%; medium-risk, 3.4%; low-risk, 0.1%) at 12 months. Opportunistic screening with AI-ECG could result in a negative ICER of -$7,439 for patients aged 65 years,…
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Current evidence most strongly supports AI-ECG for low-LVEF detection, where pragmatic randomized implementation and early economic data are available.
“Current evidence most strongly supports low-LVEF detection, where pragmatic randomized implementation and early economic data are available.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [17]
In a Taiwanese prospective outpatient study of opportunistic AI-ECG screening for asymptomatic left ventricular dysfunction, the strategy yielded a negative incremental cost-effectiveness ratio of -$7,439 for 65-year-old patients and remained cost-saving across age groups in sensitivity analyses.
“In a Taiwanese prospective outpatient study of opportunistic AI-ECG screening for asymptomatic left ventricular dysfunction, the strategy yielded a negative incremental cost-effectiveness ratio (ICER) of -$7,439 for 65-year-old patients and remained cost-saving across age groups in sensitivity analyses.17”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [17]
Cites
Roger, V. L. (2013) · Circulation Research · doi:10.1161/circresaha.113.300268 · Cited by 1,728 · see citing works
Natural history of asymptomatic left ventricular systolic dysfunction in the community
Wang, T. J., Evans, J. C., Benjamin, E. J., Levy, D., LeRoy, E. C., & Vasan, R. S. (2003) · Circulation · doi:10.1161/01.cir.0000085166.44904.79 · Cited by 633 · see citing works
The Epidemiology of Asymptomatic Left Ventricular Systolic Dysfunction: Implications for Screening
Wang, T. J., Levy, D., Benjamin, E. J., & Vasan, R. S. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-11-200306030-00012 · Cited by 229 · see citing works
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Hsieh, P.-H., Lin, C., Lin, C.-S., Liu, W.-T., Lin, T.-K., Tsai, D.-J., Hung, Y.-J., Chen, Y.-H., Lin, C.-Y., Lin, S.-H., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01735-7 · Cited by 11 · see citing works
Self-citationTang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
- Authors
- Liu, W.-T., Hsieh, P.-H., Lin, C.-S., Fang, W.-H., Wang, C.-H., Tsai, C.-S., Hung, Y.-J., Hsieh, C.-B., Lin, C., & Tsai, D.-J.
- Affiliations
- Tri-Service General Hospital (TW)
- National Defense Medical Center (TW)
- Zuoying Armed Forces General Hospital (TW)
- Fu Jen Catholic University (TW)
- Corresponding author
- Tsai, Dung-Jang
- ORCID
- Venue
- Canadian Journal of Cardiology
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 40
- Issue
- 7
- Pages
- 1310-1321
- ISSN
- 0828-282X (ISSN-L)
- OpenAlex
- W4389570818
- Cited by
- 15
- References
- 3
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 3
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Canadian Journal of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science and Technology Council (NSTC 112-2321-B-016-003, 112-2222-E-030-002-MY2)
- Tri-Service General Hospital (TSGH-D-113054, C202105049)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac pacing and defibrillation studies
- Keywords
- asymptomatic left ventricular dysfunction, opportunistic screening, cost-effectiveness, left ventricular ejection fraction, risk stratification, heart failure prevention, incremental cost-effectiveness ratio
- MeSH terms
- Aged, Algorithms, Artificial Intelligence, Cost-Benefit Analysis, Electrocardiography, Female, Humans, Male, Mass Screening, Middle Aged, Prospective Studies, ROC Curve, Taiwan, Ventricular Dysfunction, Left, Risk Assessment, Asymptomatic Diseases
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